Prediction Model of Vibration Feature for Equipment Maintenance Based on Full Vector Spectrum

Establishing a prediction model is a key step for the implementation of prognostic and health management. The prediction model can be used to forecast the change trend of the characteristics of the vibration signal and analyze the potential failure in the future. Taking the vibration of power plant steamturbine as an example, the full vector fusion and fault prediction were studied. Due to the fact that the evaluation of themachine fault with only one transducermay result in a fault judgement with partiality, an information fusionmethod based on the theory of full vector spectrumwas adopted to extract the vibration feature. An autoregressive prediction model was established. The collected vibration signals with pairing channels were fused. The time sequence of the fused vectors and spectrums were used to build the prediction model. The amplitude of main vector of rotating frequency and spectrum order structure were analyzed and predicted. The uncertainty of the spectrum structure can be eliminated by the information fusion. The reliability of the fault prediction was improved. The study on vibration prediction model system laid a technical foundation for the fault prognostic research.